Hierarchical health decision support system and method

Abstract

According to various embodiments, a hierarchical health decision support system (HDSS) configured to receive data from one or more wearable medical sensors (WMSs) is disclosed. The system includes a clinical decision support system, which includes a diagnosis engine configured to generate diagnostic suggestions based on the data received from the WMSs. The HDSS is configured with a plurality of tiers to sequentially model general healthcare from daily health monitoring, initial clinical checkup, detailed clinical examination, and postdiagnostic treatment.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to provisional application 62 / 442,756, filed on Jan. 5, 2017, which is herein incorporated by reference in its entirety.BACKGROUND OF THE INVENTION[0002]The present invention relates generally to health decision support systems and, more particularly, to a hierarchical health decision support system that integrates health data from wearable medical sensors into a clinical decision support system to individually track diseases based on a multi-tier structure.[0003]Fostered by modern healthcare, human life expectancy has increased by five years in the past two decades. The healthcare system nurtures both physical and mental health of the population through clinical services and physician expertise, along with advancements in drug and prescription management. However, it also faces significant challenges. The annual admissions to registered hospitals in the U.S. have stayed above 30 million since 2014. U.S. h...

AI Technical Summary

Benefits of technology

The patent describes a system that uses data from wearable medical sensors to provide healthcare recommendations to users. The system is designed to guide users through a process of evaluating physiological signals to diagnose diseases and provide post-diagnostic support. The patent also includes a method for generating a disease diagnosis module by constructing a training table and generating decision-maker modules. Overall, the system and method offer a hierarchical approach to healthcare decision-making based on real-time monitoring and analysis of physiological signals.

Problems solved by technology

However, it also faces significant challenges.

Despite years of remarkable progress, the healthcare system is still far from optimal.

Some of the suboptimality is due to challenges like lack of adequate cancer treatments that arise from limited knowledge of the fundamental science involved.

Although the preventability of these deaths has been questioned, the consensus remains that PMEs have a severe detrimental impact on patients.

Moreover, since these studies only take in-patient records into account, the actual impact of PMEs may be much worse.

Reducing PMEs through human inspections consumes a huge amount of effort with only weak impact.

The dominant cause of PMEs is poorly-designed human-machine interfaces.

However, conventional CDSSs are still restricted to the clinical domain.

These CDSSs have very limited access to a patient's health status after the patient leaves the clinic, resulting in several deficiencies.

However, a patient may not notice or remember all previous disease symptoms.

Another challenge for conventional CDSSs is the non-uniformity of diagnoses offered by doctors.

Even though sharing experiences among doctors is possible through academic conferences and global summits, the standard deviation in doctor prescriptions can be large.

In the past 10 years, advancements in low-power sensors and signal processing techniques have led to many disruptive WMSs.

Unfortunately, a comprehensive WMS-based information framework for health monitoring of multiple diseases is still nonexistent, greatly impeding the impact of WMSs in CDSSs.

However, this approach is too weak to capture enough information for the more challenging task of medical diagnosis.

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